Each year, tornadoes tear across the United States, causing numerous deaths and physical damage to the environment and infrastructure. The National Weather Service estimates there were about 1,475 by mid-2011 alone, outpacing the last decade's yearly average of 1,274. Many of these spinning giants strike in the plains states between the Rocky and Appalachian Mountains. So researchers at the University of Oklahoma in the heart of Tornado Alley want to know how tornadoes form and how to better predict them. Read more in this Discovery.
Credit: Amy McGovern, School of Computer Science, University of Oklahoma
A team of NSF-funded scientists at Southern Methodist University's Intelligent Data Analysis Lab (IDA) have developed a new forecasting algorithm called the Prediction Intensity Interval model for Hurricanes (PIIH), to help better predict hurricane intensity. Read more in this Discovery.
Credit: Kenzie Schott, Southern Methodist University
Thousands of residents were evacuated and 83 of Iowa's 99 counties were declared disaster areas in June 2008. The University of Iowa (UI), caught in the path of rising flood waters, suffered hundreds of millions of dollars in damages. Witold Krajewski and his colleagues at UI gained a rare opportunity to study the geophysical aspects of flooding in real time and to analyze the after-effects of flooding on residents. Read more in this Discovery.
Credit: IIHR--Hydroscience and Engineering
To better understand tornadoes, scientists with the help of the NSF, embarked on a quest to unravel the mysteries of tornadoes. The project is called VORTEX2, but it could also be called the amazing chase. For five weeks in the spring of 2009, and again in spring 2010, 100 researchers and scientists from 16 universities deployed about 40 vehicles armed with high tech equipment to measure and probe tornadoes and tornado development. Find out more in this Science Nation video.
Credit: Science Nation, National Science Foundation
Computer models developed at the National Center for Atmospheric Research (NCAR) help us understand how weather patterns develop. This visual was created from data generated by a tornado simulation calculated on the National Center for Supercomputing Applications (NCSA) IBM p690 computing cluster. Find out more in this Special Report.
Credit: Bob Wilhelmson, NCSA and the University of Illinois at Urbana-Champaign; Lou Wicker, National Oceanic and Atmospheric Administration's National Severe Storms Laboratory; Matt Gilmore and Lee Cronce, University of Illinois atmospheric science department. Visualization by Donna Cox, Robert Patterson, Stuart Levy, Matt Hall and Alex Betts, NCSA
The Division of Information and Intelligent Systems (IIS) of the Computer and Information Science and Engineering Directorate studies the interrelated roles of people, computers and information. IIS supports research and education activities that develop new knowledge about the role of people in the design and use of information technology; increase our capability to create, manage, and understand data and information in circumstances ranging from personal computers to globally-distributed systems and advance our understanding of how computational systems can exhibit the hallmarks of intelligence.
Researchers at Atmospheric and Environmental Research, Inc., (AER) and the Massachusetts Institute of Technology (MIT) are taking winter weather forecasting beyond El Nino by investigating the relationship between Siberian snow cover in fall months, and Northern Hemisphere climate variability during the winter. A forecast model developed by AER scientist Judah Cohen has achieved on-target forecasts for major cities in the industrialized countries.
March 26, 2012
Game Changer Research Aims to Forecast Tornadoes
Researcher is using supercomputers, data mining and meteorology to move us closer to reliable tornado forecasting
Tornadoes claim hundreds of lives and cause billions of dollars in damages in the United States. But the tornado outbreak across the South on April 27, 2011, was startling, even for veteran forecasters such as Greg Carbin at the National Oceanic and Atmospheric Administration (NOAA) Storm Prediction Center (SPC) in Norman, Okla.
"Through the 24-hour loop here, almost 200 tornadoes had occurred in that period of time and, unfortunately, over 315 fatalities. Primarily Alabama was hit hardest but also fatalities in Tennessee, Georgia, Mississippi, and Virginia for this event," says Carbin.
As the warning coordination meteorologist at SPC, he would like to see tools that could help predict these killer storms.
"So, that was sobering, you know. Why? Why so many fatalities? Why so much destruction?" asks Carbin.
With support from the National Science Foundation (NSF), computer scientist Amy McGovern at the University of Oklahoma is working to find answers to key questions about tornado formation. Why do tornadoes occur in some storms, but not in others?
"The problem is that if you need to understand the atmosphere, there are a whole lot of variables out there," says McGovern. "There's pressure, there's temperature, there's the wind vector. And none of the radars, none of the current sensing instruments can get that at the resolution that we really need to fundamentally understand the tornadoes," she says.
While video from storm chasers and data from Doppler radar can help meteorologists understand some aspects of tornadoes, McGovern uses different, powerful tools: supercomputers, and a technology known as data mining.
"Data mining is finding patterns in very large datasets. Humans do really, really well at finding patterns in small datasets but fail miserably when the datasets get as large as we're talking about," she says.
McGovern and her team don't just study "real" storms. They create supercomputer simulations to analyze how constantly changing storm components interact. And each storm they create may generate a terabyte of data.
"What we're doing with our simulations is actually being able to sense all of these fundamental variables every 50 to 75 meters," she explains.
She works with many weather experts, including research meteorologist Rodger Brown, at the National Severe Storms Laboratory. He's helping to sort out what's usable information in the simulation, and what's not needed within the enormous amount of data. He points out some of the "unknowns" on a computer animation.
"There's some of the bad noise bouncing off the edge of the grid. Somehow, it's noise being amplified, some sort of gravity waves or something. So we're going to have to do some more experimentation to find out what the problem is," says Brown.
Even though there are still many questions to answer with these simulations, McGovern says there's no hardware right now that could be deployed in a tornado to get so many observations. And even if it did exist, it would likely get destroyed in just about any storm.
Kelvin Droegemeier, professor of meteorology and vice president of research at the University of Oklahoma, has studied severe weather in "Tornado Alley" for many years. He's excited by the collaboration of meteorology and computer science.
"It's a game-changer, complete game-changer. Radar leads off basically with detecting something that's already present; the numerical model gives us the opportunity to actually project it and predict it far in advance," he says.
"So, instead of warning on a detection based on radar [and] some visual sighting, you're actually warning based on what a numerical forecast model will tell you. So, imagine a tornado warning being issued before a storm is even present in the sky," says Droegemeier.
"Amy McGovern has pursued interdisciplinary research early in her career," says Maria Zemankova, program director for the Information and Intelligent Systems (IIS) Division of the Directorate for Computer and Information Science and Engineering (CISE) at the NSF. "A computer scientist, her CAREER award was actually co-funded by CISE and NSF's Geosciences Directorate. It's impressive how she's been able to forge collaborations among researchers in both disciplines of computer science and the atmospheric sciences to yield actionable results."
But while the ability to predict tornados so far in advance would be a breakthrough, there's another variable that is even harder to predict than a dangerous storm: human behavior.
"That's a very interesting challenge that also brings in the whole social behavioral issues of, how would people react. Would they kind of dismiss that as, well, there's not even a storm, I looked outside, the skies are clear?" says Droegemeier.
He notes what meteorology students are learning today is very different from what he learned. And it involves not only collaboration with computer scientists like McGovern, or the electrical engineers building new radars, but other experts as well.
"Learn how to talk to social scientists, learn how to communicate your science outcomes to the Kiwanis Club, to other communities that really need to understand what we're doing. Because it's only through that broad understanding, I think, that we can really solve the problem of tornadoes and severe storms and other kinds of hazardous weather," says Droegemeier.
As a professor of computer science, McGovern says research like this, designed to improve tornado prediction, makes an important point in her classes.
"Computer science can really make a difference to the real world, and I'm trying to bring that to the students," she says.
McGovern also stressed how critical the level of trust needs to be in working with the forecasters who will be making life-saving warnings to thousands of people.
"I can see it has to be really easy to use, and they have to understand it deeply. They don't want something that just says, 'here's the probability.' It has to be something that we can print out, draw, use some form to say to the meteorologist, 'this is exactly what we see is going on," she says.
She has spent time observing and interacting with forecasters and some of the technologies they use at the Storm Prediction Center--and it's been an eye-opener.
"It was in the Hazardous Weather Testbed Office, and I got to see them using the different models, and saying, 'this model says this, this is why we think this is right, this model says this, but we don't believe the model and this is why.' So I got to see the humans using the technology and understanding why they like it, and why they don't, and to me that was pretty darn enlightening!" says McGovern.
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